magrittr
dplyr
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magrittr | dplyr | |
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10 | 40 | |
951 | 4,645 | |
0.0% | 0.6% | |
2.3 | 7.4 | |
about 1 year ago | 15 days ago | |
R | R | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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magrittr
- This is not a pipe - René Magritte
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Six programming languages I’d like to see
R (yes, the statistics language) has exactly this.
You can literally extract the body of a function as a list of "call" objects (which are themselves just dressed-up lists of symbols), inject/delete/modify individual statements, and then re-cast your new list to a new function object.
I don't know why the original devs thought this was necessary or even desirable in a statistics package, but it turns out to be a lot of fun to program with. It has also made possible a wide variety of clever and elegant custom syntaxes, such as a pipe infix operator implemented as a 3rd-party library without any custom language extensions [0]. The pipe infix operator got so popular that it was eventually made part of the language core syntax in version 4.1 [1].
- Hadley is pro- base pipe.
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Functional pipes in python like %>% from R's magrittr
In R (thanks to magrittr) you can now perform operations with a more functional piping syntax via %>%. This means that instead of coding this:
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Question about dot notation
Try reading the documentation for magrittr.
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When did WG21 decide this is what networking looks like?
Related note: the statistical programming language R has a library named magrittr to support the pipe operator.
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How can I find the data entry of the row after one found?
About the pipe (%>%) symbol, it's provided by the magrittr package. The package documentation details how to use the pipe operator.
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Best practice for chaining nested functions?
I was wondering what some good ways are to handle nested function calls without chaining them in long, ugly nested statements. I am looking for functionality similar to the pipe forward operator %>% in magrittr/R or |> in F#.
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I much prefer `data.action()` to `action(data). Is it an r/unpopularopinion?
You may like R: https://magrittr.tidyverse.org
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What's so "tidy" about tidyverse?
Agreed on everything else you said (especially the type safety stuff, it massively helps in production), but one correction: magrittr is absolutely in the tidyverse suite. It's not considered one of its "core" packages that it visibly tells you it loads, but magrittr is loaded when calling library(tidyverse) and development of the package is handled by the tidyverse team under their Github account: https://github.com/tidyverse/magrittr
dplyr
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Show HN: Open-source, browser-local data exploration using DuckDB-WASM and PRQL
That's great feedback, thanks!
This tool definitely comes from a place of personal need - beyond just handling large files, I've also never really gelled well with the Excel/Google Sheet model of changing data in place as if you were editing text. I'm a Data Scientist and always preferred the chained data transforms you see in things like dplyr (https://dplyr.tidyverse.org/) or Polars (https://pola.rs/) and I feel this tool maps very closely to the chained model.
Also, thank you for the feature requests! Those would all be very useful - we'll put them on the roadmap.
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IS it possible for a R package to set an R option that only affects that package?
There's an example of how to use zzz.R with a .onload() function to set options in the dplyr code base: https://github.com/tidyverse/dplyr/blob/bbcfe99e29fe737d456b0d7adc33d3c445a32d9d/R/zzz.r
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Calculation within a data table by calling on specific values in two columns
Look at the tidyverse, especially the case_when or mutate functions.
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PSA: You don't need fancy stuff to do good work.
Before diving into advanced machine learning algorithms or statistical models, we need to start with the basics: collecting and organizing data. Fortunately, both Python and R offer a wealth of libraries that make it easy to collect data from a variety of sources, including web scraping, APIs, and reading from files. Key libraries in Python include requests, BeautifulSoup, and pandas, while R has httr, rvest, and dplyr.
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Creating data frame
It looks like your syntax is wrong. I think you’re trying to calculate a new variables in your data frame, or alter an existing column in a data frame. Have a look at the select() function in this reference for the proper syntax to use. https://dplyr.tidyverse.org/ Does that help?
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I'm designing a shirt for a friend, it has 4 embroidered images of things they like/do. One thing is coding, they use R... I'm wondering two things. 1) What's a good image or piece of code or something that I should use? and 2) should I even add it to the design the shirt?
A lot of populat libraries have their own logos. Maybe one of them would be good. Check out dplyr for example: https://dplyr.tidyverse.org/
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Anyone use Python for statistics, particularly DOE or QA/QC? What are your thoughts?
I hope you give it a try when you get a chance: https://dplyr.tidyverse.org/
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Rstudio tidyverse help!
You can read up on the dplyr-verbs here, which I strongly suggest for your exam! In the code examples, you can simply click on any function you don't understand and it will take you directly to the documentation. Good Luck!
- Beginner question
- osdc-2023-assignment1
What are some alternatives?
scenebuilder - Scene Builder is a visual, drag 'n' drop, layout tool for designing JavaFX application user interfaces.
worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
kitten - A statically typed concatenative systems programming language.
Rustler - Safe Rust bridge for creating Erlang NIF functions
power-fx-host-samples - Samples for hosting Power Fx engine.
ggplot2 - An implementation of the Grammar of Graphics in R
libuv-tutorial - http://nikhilm.github.io/uvbook/
nx - Multi-dimensional arrays (tensors) and numerical definitions for Elixir
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
FiraCode - Free monospaced font with programming ligatures
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more